Inflate Relative Abundances to Integer Counts

Scaling a matrix of proportions (or counts) to a new target depth, rounding to integers while preserving the original total abundance sum exactly.

Usage

biom_inflate(biom, depth = NULL, clone = TRUE)

Arguments

biom

An rbiom object, or any value accepted by as_rbiom().

depth

The target library size (sum) for each sample. Must be an integer greater than 0. If NULL (the default), the depth is estimated per-sample using the "Singleton Peak Heuristic". See suggest_inflate_depths() for algorithm details.

clone

Create a copy of biom before modifying. If FALSE, biom is modified in place as a side-effect. See speed ups for use cases. Default: TRUE

Value

An rbiom object.

Rounding (Largest Remainder Method)

To ensure the sum of the resulting counts equals the target depth exactly (avoiding drift caused by simple rounding), this function uses the Largest Remainder Method (also known as the Hare-Niemeyer method).

It assigns the integer part of the scaled value to each feature, and then distributes the remaining counts to the features with the largest fractional parts.

See also

suggest_inflate_depths() for details on how target depths are estimated when depth = NULL.

Other transformations: biom_relativize(), biom_rescale(), modify_metadata, rarefy(), slice_metadata, subset(), with()

Examples

    library(rbiom)
    
    biom <- hmp50[1:5]
    sample_sums(biom)
#> HMP01 HMP02 HMP03 HMP04 HMP05 
#>  1660  1371  1353  1895  3939 
    
    biom <- biom_relativize(biom)
    sample_sums(biom)
#> HMP01 HMP02 HMP03 HMP04 HMP05 
#>     1     1     1     1     1 
    
    biom <- biom_inflate(biom)
    sample_sums(biom)
#> HMP01 HMP02 HMP03 HMP04 HMP05 
#>  1267  1395  1032  1157  4007